Beyond Detection: Large Language Models and Next-Generation Cybersecurity

Abstract

The Integrating Contextual and Adaptive Cyber Defense Systems The accelerating evolution of cyber threats, seen both in terms of their complexity and frequency of attempts, makes it imperative to move away from traditional, reactive defense structures and towards intelligent, adaptive and proactive cyber defense strategies. Large Language Models (LLMs) (such as GPT this-will-be-a-zillionth-of-a-second-type-here or BERT derivatives) represent powerful new capabilities developed for understanding, analyzing, and generating human-like language with contextual depth enabling a new frontier in the land of cyber defense. This survey investigates the influence of LLMs on multiple key technical areas of cybersecurity including software and system security, network security, content moderation, hardware security, and blockchain security. It showcases applications of LLMs in tasks in vulnerability detection and prevention, phishing, malware detection and analysis, and intrusion detection; as well as smart contract auditing and adversarial attack simulation. The paper goes more in-depth on technical constructs like zero-shot and few-shot threat hunting, prompt engineering for scenario modeling, and retrieval-augmented generation for real-time intelligence. Task differences are captured in knowledge retrieval from external sources, enabling more contextual learning and task adaptability, inter-model interaction, and augmentation through domain-specific fine-tuning. While promising, LLMs pose open challenges, such as hallucination, adversarial misuse, generalization problems, and ethical issues around privacy and accountability. The implications of such comparative analysis across domains highlight the strengths as well as ‘emerging risk’ associated with LLM systems at mission critical environments. The survey ends with a highly visionary section on the future opportunities around future self-healing systems, autonomous cyber agents, and the future of LLMs that can not only detect threats but also predict, simulate, and respond automatically to threats. This work serves as a foundational roadmap for researchers and practitioners for the use of LLMs for the next generation of resilient cybersecurity tools.